Technology decisions that convert to outcomes

Compare architecture, platform, vendor and build/buy/partner options against business needs, integration, technical debt, scalability, operations and total cost of ownership. Outputs can include a current-state assessment, scored options, target-state direction, migration risks and an executive decision brief.

• Facing a platform replacement or modernization decision
• Multiple competing vendor proposals with no independent evaluation framework
• Architecture drift is creating operational or delivery risk

Assess potential AI use cases across value, feasibility and control. Rank opportunities, identify data, infrastructure, integration, operating-model and governance gaps, compare build/buy/partner choices, and sequence the evidence needed before production investment.

• AI pilots are not converting to production value at scale
• Leadership and engineering are not aligned on AI investment priorities
• Evaluating AI investment before committing to a program

Review architecture, delivery health, technical debt, security, scalability, data and integration risk to support a technology investment decision. Outputs can include a risk register, evidence questions, options and priorities. This is not financial, legal or investment assurance.

• Evaluating a technology acquisition or investment target
• Assessing a vendor or partner before a long-term dependency
• Board or investor requirement for independent technical assurance

Review objectives, architecture, dependencies, delivery model, quality, release risk and ownership for an at-risk program. Produce evidence-based findings, recovery options, priorities and an executive brief without promising a universal recovery result.

• Transformation program has stalled or missed key milestones
• Leadership and delivery teams have lost confidence in the current plan
• Quality or production incidents are increasing despite ongoing delivery

Review data sources, quality, lineage, architecture, access, ownership, governance and the operating model. Define current-state findings, target principles, priority decisions and a sequenced roadmap.

• AI or analytics initiatives are blocked by data quality or availability problems
• Data ownership and governance are unclear across teams or systems
• Preparing for a data platform consolidation or migration

Review identity, data handling, architecture, delivery practices, logging, incident readiness and applicable obligations. Identify gaps, priorities and accountable next actions. This pathway is not a certification or legal opinion.

• Security requirements are blocking a technology decision or acquisition
• Compliance obligations are creating delivery risk or uncertainty
• Leadership needs a clear view of security risk before a major investment

Review cloud reliability, security, operations, scalability and cost drivers. Deliver findings, target patterns, risks and a remediation roadmap without guaranteeing savings, reliability or compliance.

• Cloud costs are growing without a clear view of what is driving the increase
• Reliability or performance incidents are recurring without a root-cause architecture review
• A workload is approaching production or a major scaling event and has never been formally reviewed